Map matching method and apparatus, electronic device and storage medium

ABSTRACT

A map matching method and apparatus, an electronic device and a storage medium are provided. The method includes: acquiring initial positioning information of a vehicle and vehicle sensor data; determining a plurality of pieces of candidate positioning information on the basis of the initial positioning information; determining a plurality of observation semantic features on the basis of the vehicle sensor data; acquiring local map information on the basis of the initial positioning information of the vehicle, the local map information including a plurality of map semantic features; for each piece of candidate positioning information: converting the plurality of map semantic features into a coordinate system of the vehicle sensor; and matching the plurality of observation semantic features with the plurality of candidate map semantic features; and determining optimal candidate positioning information of the vehicle and a matching pair corresponding to the optimal candidate positioning information.

The present application claims priority to Chinese Patent ApplicationNo. 2020110314570, titled “MAP MATCHING METHOD AND APPARATUS, ELECTRONICDEVICE AND STORAGE MEDIUM”, and filed on Sep. 27, 2020 to China NationalIntellectual Property Administration, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

Embodiments of the present application relate to the field ofintelligent driving technologies, and particularly to a map matchingmethod and apparatus, an electronic device and a non-transientcomputer-readable storage medium.

BACKGROUND

In the field of intelligent driving technologies, vehicle positioningtechnology plays an important role in both aided driving and automateddriving. At present, mainstream vehicle positioning technologies includea vslam (visual simultaneous localization and mapping) technology basedon vision, a Islam (laser simultaneous localization and mapping)technology based on laser radar, and the like. According to thesemethods, it is usually necessary to build a dense positioning map inadvance, and descriptors, intensity and other information for matchingare stored, thus occupying a lot of storage resources.

In order to reduce the size of the positioning map and improverobustness in some scenes at the same time, positioning is realized onthe basis of a vector semantic map in some positioning solutions.However, although the sparsity of the vector map greatly reduces thesize thereof, the lack of descriptors and other information alsochallenges the way to achieve efficient and high-accuracy real-timematching.

The description of the discovery process of the above problems is onlyused to assist in understanding the technical solutions of the presentapplication, and does not represent an admission that the above contentsbelong to the prior art.

SUMMARY

In order to solve at least one problem in the prior art, at least oneembodiment of the present application provides a map matching method andapparatus, an electronic device and a non-transient computer-readablestorage medium.

According to a first aspect, the embodiments of the present applicationprovide a map matching method, wherein the method includes:

-   -   acquiring initial positioning information of a vehicle and        vehicle sensor data;    -   determining a plurality of pieces of candidate positioning        information on the basis of the initial positioning information;    -   determining a plurality of observation semantic features on the        basis of the vehicle sensor data;    -   acquiring local map information on the basis of the initial        positioning information, the local map information comprising a        plurality of map semantic features;    -   for each piece of candidate positioning information:    -   converting the plurality of map semantic features into a        coordinate system of the vehicle sensor on the basis of the        candidate positioning information, so as to obtain a plurality        of candidate map semantic features under the coordinate system;        and    -   matching the plurality of observation semantic features and the        plurality of candidate map semantic features, so as to obtain        matching pairs; and    -   determining optimal candidate positioning information of the        vehicle and a matching pair corresponding to the optimal        candidate positioning information on the basis of the matching        pairs corresponding to each piece of candidate positioning        information.

According to a second aspect, the embodiments of the present applicationfurther provide a map matching apparatus, wherein the apparatusincludes:

-   -   an acquisition unit configured for acquiring initial positioning        information of a vehicle and vehicle sensor data; and acquiring        local map information on the basis of the initial positioning        information, the local map information including a plurality of        map semantic features;    -   a first determining unit configured for determining a plurality        of pieces of candidate positioning information on the basis of        the initial positioning information; and determining a plurality        of observation semantic features on the basis of the vehicle        sensor data;    -   a matching unit configured for, for each piece of candidate        positioning information:    -   converting the plurality of map semantic features into a        coordinate system of the vehicle sensor on the basis of the        candidate positioning information, so as to obtain a plurality        of candidate map semantic features under the coordinate system;        and    -   matching the plurality of observation semantic features and the        plurality of candidate map semantic features, so as to obtain        matching pairs; and    -   a second determining unit configured for determining optimal        candidate positioning information of the vehicle and a matching        pair corresponding to the optimal candidate positioning        information on the basis of matching pairs corresponding to each        piece of candidate positioning information.

According to a third aspect, the embodiments of the present applicationfurther provide an electronic device, including: a processor and amemory; the processor being configured for executing the steps of themap matching method according to the first aspect by calling a programor instruction stored in the memory.

According to a fourth aspect, the embodiments of the present applicationfurther provide a non-transient computer-readable storage medium forstoring a program or instruction, wherein the program or instructionenables a computer to execute the steps of the map matching methodaccording to the first aspect.

It can be seen that in at least one embodiment of the presentapplication, the plurality of observation semantic features aredetermined on the basis of the vehicle sensor data, and the local mapinformation (the local map information includes the plurality of mapsemantic features) is acquired on the basis of the initial positioninginformation of the vehicle and the plurality of pieces of candidatepositioning information (including the initial positioning information)are determined; then, the plurality of observation semantic features arematched with the plurality of candidate map semantic features, so as toobtain the matching pairs; and therefore, the optimal candidatepositioning information of the vehicle and the matching paircorresponding to the optimal candidate positioning information on thebasis of the matching pairs corresponding to each piece of candidatepositioning information are determined.

The embodiments of the present application are suitable for a vectorsemantic map, achieves real-time matching of observation features andmap features by only using vector information (such as information ofmap semantic features) of the vector semantic map, does not depend onadditional data such as additional descriptors and intensity, andachieves a good matching effect on the basis of reducing storagerequirements and computing power use. In addition, the embodiments ofthe present application have no special requirements for sensor types (acamera, a laser radar, and the like, may be applicable).

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of theembodiments of the present application or the prior art, the figuresthat are required to describe the embodiments or the prior art will bebriefly introduced below. Apparently, the figures that are describedbelow are some embodiments of the present application, and those ofordinary skills in the art can obtain other figures according to thesefigures without paying creative work.

FIG. 1 is an exemplary scenario diagram of a matching problem accordingto the embodiments of the present application

FIG. 2 is an exemplary architectural diagram of an intelligent drivingvehicle according to the embodiments of the present application;

FIG. 3 is an exemplary block diagram of an intelligent driving systemaccording to the embodiments of the present application;

FIG. 4 is an exemplary block diagram of a map matching apparatusaccording to the embodiments of the present application;

FIG. 5 is an exemplary block diagram of an electronic device accordingto the embodiments of the present application;

FIG. 6 is an exemplary flowchart of a map matching method according tothe embodiments of the present application;

FIG. 7 is an example diagram of a semantic-Euclidean distance matrixaccording to the embodiments of the present application;

FIG. 8 is an example diagram of a distance ranking matrix determinedbased on the semantic-Euclidean distance matrix shown in FIG. 7 ; and

FIG. 9 is an example diagram of a matrix obtained after updating thedistance ranking matrix shown in FIG. 8 .

DETAILED DESCRIPTION OF THE EMBODIMENTS

In order to make the above objects, features and advantages of thepresent application be more clearly understood, the present applicationwill be described in further detail below with reference to the drawingsand embodiments. It can be understood that the described embodiments arepart of the embodiments of the present application, rather than all ofthe embodiments. The specific embodiments described herein are merelyillustrative of the present application, but are not intended to limitthe present application. Based on the embodiments in the presentapplication, all other embodiments obtained by those of ordinary skillsin the art without going through any creative work shall fall within thescope of protection of the present application.

It should be noted that relational terms herein such as “first” and“second” are used merely to distinguish one entity or operation fromanother entity or operation, and do not necessarily require or implythere is any such relationship or order between these entities oroperations.

In order to perform map matching in real time during an intelligentdriving process, in conjunction with FIG. 1 , the embodiments of thepresent application describe matching problems as follows:

In FIG. 1 , a set M{m1, m2} and a set M′ {m′1, m′2} are given, as manymatching elements as possible are found in M′ for elements in M underthe constraint of minimum distance to form matching pairs, wherein eachmatching pair includes one element in M and one element in M′.

In FIG. 1 , there are two matching pairs: (m1, m′2) and (m2, m′1). Itwill be appreciated that, since under the constraint of minimumdistance, a distance between m1 and m′2 is less than a distance betweenm1 and m′1, and likewise, a distance between m2 and m′1 is less than adistance between m2 and m′2.

Based on the above description of the matching problems, the set M maybe understood as a set of observation features and the set M′ may beunderstood as a set of map features. In some embodiments, theobservation feature may be a real-time observation feature, whether theobservation feature or the map feature is a semantic feature, that is,the observation feature is an observation semantic feature, and the mapfeature is a map semantic feature.

The observation semantic features may be understood as an observationsemantic feature of a determined target for positioning on the basis ofvehicle sensor data, for example, the vehicle sensor data is image data,the image data is processed through a target detection algorithm, acategory and a position of a target included in the image can bedetermined, and the target may be understood as an observation semanticfeature. For example, the image includes lane lines, traffic markings(such as straight, left-turn, right-turn, and the like), traffic signals(i.e., traffic lights), traffic signs, and the like, which areobservation semantic features of the target for positioning.

The map semantic feature may be understood as a semantic feature of atarget included in a map (for example, vector map), for example, a laneline, a traffic marking, a traffic signal, a traffic sign and the likein a map, which are all map semantic features of the target forpositioning. In some embodiments, in order to facilitate the acquisitionof the map semantic features from the map, the map may be pre-processedsuch that the map includes information of the map semantic features,such as information related to the semantic features of the map, such assemantic labels and positions including map semantic features in themap, so as to obtain information of the map semantic features from themap while obtaining the map.

In map matching, the most matching pairs are found according to acertain constraint condition, and then map features corresponding todifferent observation features are determined, thereby providing a basisfor subsequent vehicle positioning. It should be noted that in the mapmatching, the constraint condition includes not only the constraint ofminimum distance, but also other constraint conditions, these constraintconditions jointly determine the determination of the matching pair, andthe content included in the constraint conditions will be described indetail below.

Therefore, the embodiments of the present application provide a mapmatching solution, which determines a plurality of observation semanticfeatures on the basis of the vehicle sensor data, and acquires local mapinformation (the local map information includes a plurality of mapsemantic features) on the basis of initial positioning information ofthe vehicle and a plurality of pieces of candidate positioninginformation (including the initial positioning information); then,matches the plurality of observation semantic features with theplurality of candidate map semantic features for each piece of candidatepositioning information, so as to obtain matching pairs; and therefore,determines optimal candidate positioning information of the vehicle anda matching pair corresponding to the optimal candidate positioninginformation on the basis of the matching pairs corresponding to eachpiece of candidate positioning information.

The embodiments of the present application are suitable for a vectorsemantic map, achieves real-time matching of observation features andmap features by only using vector information (such as information ofmap semantic features) of the vector semantic map, does not depend onadditional data such as additional descriptors and intensity, andachieves a good matching effect on the basis of reducing storagerequirements and computing power use. In addition, the embodiments ofthe present application have no special requirements for sensor types (acamera, a laser radar, and the like, may be applicable).

In some embodiments, a semantic-Euclidean distance between the mapsemantic feature and the observation semantic feature may be determined,and then a distance matrix may be determined, so as to implement vectorsemantic map matching based on the distance matrix. In some embodiments,the distance matrix-based matching method is widely applicable tovarious matching scenarios that can define metric distances.

The embodiments of the present application may be applied to anintelligent driving vehicle, and may also be applied to an electronicdevice. The intelligent driving vehicle is a vehicle carrying differentlevels of intelligent driving systems, and the intelligent drivingsystems include, for example, an unmanned driving system, an aideddriving system, a driving assistance system, a highly-automatic drivingsystem, a full self-driving vehicle, and the like. The electronic deviceis installed with an intelligent driving system, for example, theelectronic device may be used to test an intelligent driving algorithm,for another example, the electronic device may be an on-vehicle device,and in some embodiments, the electronic device may also be applied toother fields. It should be understood that the application scenarios ofthe embodiments of the present application are only examples orembodiments of the present application, and that it is also possible forthose of ordinary skills in the art to apply the present application toother similar contexts without going through creative work. In order tobe more clearly described below, in the embodiments of the presentapplication, the intelligent driving vehicle is used as an example todescribe a map matching method, a map matching apparatus, an electronicdevice, or a non-transient computer-readable storage medium.

FIG. 2 is an exemplary architectural diagram of an intelligent drivingvehicle according to the embodiments of the present application. Asshown in FIG. 2 , the intelligent driving vehicle includes: a sensorgroup, an intelligent driving system 200, an underlying execution systemof the vehicle, and other components that may be used to drive thevehicle and control the operation of the vehicle, such as a brake pedal,a steering wheel, and an accelerator pedal.

The sensor group is used to collect data of an external environment ofthe vehicle and detect position data of the vehicle. The sensor groupincludes, but is not limited to, at least one of a camera, a lidar, amillimeter-wave radar, an ultrasonic radar, a GPS (Global PositioningSystem), and an IMU (Inertial Measurement Unit).

In some embodiments, the sensor group is also used for collectingkinetic data of the vehicle. For example, the sensor group furtherincludes, but is not limited to, at least one of a wheel speed sensor, aspeed sensor, an acceleration sensor, a steering wheel angle sensor anda front wheel angle sensor.

The intelligent driving system 200 is configured to acquire sensing dataof the sensor group, wherein the sensing data includes, but is notlimited to, an image, a video, a laser point cloud, a millimeter wave,GPS information, a vehicle state, and the like. In some embodiments, theintelligent driving system 200 performs environmental perception andvehicle positioning on the basis of the sensing data to generateperception information and a vehicle pose. The intelligent drivingsystem 200 performs planning and decision on the basis of the perceptioninformation and the vehicle pose to generate planning and decisioninformation. The intelligent driving system 200 generates a vehiclecontrol instruction on the basis of the planning and decisioninformation, and issues the vehicle control instruction to theunderlying execution system of the vehicle.

In some embodiments, the intelligent driving system 200 may be asoftware system, a hardware system or a system combining hardware andsoftware. For example, the intelligent driving system 200 is a softwaresystem running on an operating system, and an on-vehicle hardware systemis a hardware system that supports the operation of the operatingsystem.

In some embodiments, the intelligent driving system 200 may interactwith a cloud server. In some embodiments, the intelligent driving system200 interacts with the cloud server through a wireless communicationnetwork (for example, including, but not limited to a GPRS network, aZigbee network, a Wifi network, a 3G network, a 4G network, a 5Gnetwork, and the like).

In some embodiments, the cloud server is used to interact with thevehicle. The cloud server may send environmental information,positioning information, control information, and other informationrequired in the intelligent driving process of the vehicle to thevehicle. In some embodiments, the cloud server may receive the sensingdata, the vehicle state information, the vehicle driving information,and the related information of the vehicle request from the vehicle end.In some embodiments, the cloud server may remotely control the vehiclebased on user settings or vehicle requests. In some embodiments, thecloud server may be a server or a server group. The server group may becentralized or distributed. In some embodiments, the cloud server may belocal or remote.

The underlying execution system of the vehicle is used to receive thevehicle control instruction and control the running of the vehicle onthe basis of the vehicle control instruction. In some embodiments, theunderlying execution system of the vehicle includes, but is not limitedto: a steering system, a braking system and a driving system. In someembodiments, the underlying execution system of the vehicle may furtherinclude an underlying controller configured to parse the vehicle controlinstruction and issue the vehicle control instruction to correspondingsystems such as the steering system, the braking system, and the drivingsystem.

In some embodiments, the intelligent driving vehicle may also include avehicle CAN bus, which is not shown in FIG. 1 , and the vehicle CAN busis connected with the underlying execution system of the vehicle.Information exchange between the intelligent driving system 200 and theunderlying execution system of the vehicle is transmitted through thevehicle CAN bus.

FIG. 3 is an exemplary block diagram of an intelligent driving system300 according to the embodiments of the present application. In someembodiments, the smart driving system 300 may be implemented as theintelligent driving system 200 in FIG. 2 or a part of the intelligentdriving system 200, and is used to control the running of the vehicle.

As shown in FIG. 3 , the intelligent driving system 300 may be dividedinto a plurality of modules, for example, may include: a perceptionmodule 301, a planning module 302, a control module 303, a map matchingmodule 304 and some other modules used for intelligent driving.

The perception module 301 is used for environmental perception andpositioning. In some embodiments, the perception module 301 is used toperform environmental perception and positioning on the basis of atleast one of the acquired sensor data, V2X (Vehicle to X, vehiclewireless communication) data, high-precision maps and other data. Theperception information may include, but is not limited to at least oneof the following: obstacle information, a road sign/mark,pedestrian/vehicle information, and a drivable area. The positioninginformation includes a vehicle pose.

The planning module 302 is used to perform route planning and decisionmaking. In some embodiments, the planning module 302 generates planningand decision information on the basis of the perception information andthe positioning information generated by the perception module 301. Insome embodiments, the planning module 302 is also used to generateplanning and decision information with reference to at least one of theV2X data, the high-precision maps and other data. The planninginformation may include, but is not limited to planning route; and thedecision making information may include, but is not limited to at leastone of the following: behaviors (for example, including, but not limitedto following, overtaking, parking, bypassing, and the like), vehicleheading, vehicle speed, expected acceleration of the vehicle, expectedsteering wheel angle, and the like.

The control module 303 is configured to generate a control instructionof the underlying execution system of the vehicle on the basis of theplanning and decision information, and issue the control instruction toenable the underlying execution system of the vehicle to control thevehicle to run. The control may include, but is not limited to: steeringof the steering wheel, a transverse control instruction, a longitudinalcontrol instruction, and the like.

The map matching module 304 is configured for determining a plurality ofobservation semantic features on the basis of the vehicle sensor data,and acquiring local map information (the local map information includesa plurality of map semantic features) on the basis of initialpositioning information of the vehicle and a plurality of pieces ofcandidate positioning information (including the initial positioninginformation); then, matching the plurality of observation semanticfeatures with the plurality of candidate map semantic features for eachpiece of candidate positioning information, so as to obtain matchingpairs; and therefore, determining optimal candidate positioninginformation of the vehicle and a matching pair corresponding to theoptimal candidate positioning information on the basis of the matchingpairs corresponding to each piece of candidate positioning information.

In some embodiments, functions of the map matching module 304 may beintegrated into the perception module 301, the planning module 302 orthe control module 303, or may also be configured as an independentmodule from the intelligent driving system 300. The map matching module304 may be a software module, a hardware module or a module integratingsoftware and hardware. For example, the map matching module 304 is asoftware system running on an operating system, and a vehicle-mountedhardware system is a hardware system that supports the operation of theoperating system.

FIG. 4 is an exemplary block diagram of a map matching apparatus 400according to the embodiments of the present application. In someembodiments, the map matching apparatus 400 may be implemented as themap matching module 304 in FIG. 3 or a part of the map matching module304.

As shown in FIG. 4 , the map matching apparatus 400 may include, but isnot limited to the following units: an acquisition unit 401, a firstdetermining unit 402, a matching unit 403 and a second determining unit404.

Acquisition Unit 401

The acquisition unit 401 is configured for acquiring related informationof the vehicle, where the related information includes, for example,information including initial positioning information, vehicle sensordata, and the like, with a direct or indirect association relationshipwith the vehicle. The initial positioning information may be priorinformation from outside exterior of the vehicle, and may also beestimated from the second determining unit 404. The vehicle sensor datamay be obtained by performing data interaction with the sensor installedon the vehicle.

In some embodiments, the acquisition unit 401 may acquire local mapinformation on the basis of the initial positioning information. In someembodiments, the acquisition unit 404 may acquire the local mapinformation from a pre-established vector semantic map on the basis ofthe initial positioning information, the local map information being apart of the vector semantic map. In some embodiments, the acquisitionunit 404 may index the vector semantic map through a fast nearestneighbor algorithm on the basis of the initial positioning informationto obtain the local map information.

The pre-established vector semantic map includes information of mapsemantic features, such as lane lines, traffic markings, trafficsignals, traffic signs and the like, which are all map semanticfeatures. The information of the map semantic features, for example,information related to the map semantic features, such as semanticlabels and positions of the map semantic features.

In this embodiment, the acquisition unit 404 can acquire the informationof the map semantic features from the local map information whileacquiring the local map information, that is, can obtain the pluralityof map semantic features included in the local map information.

First Determining Unit 402

The first determining unit 402 is configured for determining a pluralityof pieces of candidate positioning information on the basis of theinitial positioning information, the plurality of candidate behaviorinformation including the initial positioning information. In thisembodiment, considering that the initial positioning informationgenerally has a large error, the map semantic features have strongsparsity, and if the initial positioning information is directly usedfor map matching, the matching accuracy is low or the effective matchingfeatures are few, so that a correct rate of subsequent map matching canbe improved or a number of effective matching features can be increasedby determining the plurality of pieces of candidate positioninginformation.

In some embodiments, the first determining unit 402 may perform discreterandom sampling on a space within a certain range of the initialpositioning information to obtain the plurality of pieces of candidatepositioning information. In some embodiments, the first determining unit402 generates n pieces of candidate positioning information includingthe initial positioning information according to a certain probabilitydistribution within a certain spatial range r of the initial positioninginformation by means of Monte Carlo random sampling. The number n of thespatial range r and the number n of the candidate positioninginformation are both related to uncertainty of the initial positioninginformation, the higher the uncertainty of the initial positioninginformation, the greater the values of r and n.

In some embodiments, the first determining unit 402 may determine aplurality of observation semantic features on the basis of the vehiclesensor data, the plurality of observation semantic features beingreal-time observation semantic features. For example, the vehicle sensordata is image data, the first determining unit 402 processes the imagedata by using a target detection algorithm, a category and a position ofa target included in the image may be determined, and the target may beunderstood as an observation semantic feature. For example, the imageincludes lane lines, traffic markings (such as straight, left-turn,right-turn, and the like), traffic signals (i.e., traffic lights),traffic signs, and the like, which are all observation semanticfeatures. It is worth noting that the above examples of the sensor dataare only used for illustration and are not used to limit the presentapplication, and in practical application, the vehicle sensor data maybe in any form (for example, laser radar data), as long as theobservation semantic features can be identified from the sensor data.

Matching Unit 403

The matching unit 403 is configured for performing map matching for eachpiece of candidate positioning information. In some embodiments, thematching unit 403, for each piece of candidate positioning information:converts the plurality of map semantic features into a coordinate systemof the vehicle sensor on the basis of the candidate positioninginformation, so as to obtain a plurality of candidate map semanticfeatures under the coordinate system; and matches the plurality ofobservation semantic features with the plurality of candidate mapsemantic features, so as to obtain matching pairs. In some embodiments,the matching unit 403 also converts the plurality of observationsemantic features into the coordinate system of the same vehicle sensor,and then matches the plurality of observation semantic features with theplurality of candidate map semantic features under the coordinate systemconverted to the same vehicle sensor to obtain the matching pairs.

In some embodiments, the matching unit 403 performs observabilityscreening on each candidate map semantic feature, that is, determineswhether the candidate map semantic feature is within a blind area of thevehicle sensor, and when the candidate map semantic feature is withinthe blind area of the vehicle sensor, determines that the candidate mapsemantic feature may not be matched with the observation semanticfeatures, should be filtered out, and does not participate in subsequentmatching.

In particular, the matching unit 403, for each piece of candidatepositioning information: removes the candidate map semantic features inthe blind area of the vehicle sensor after converting the plurality ofmap semantic features into the coordinate system of the vehicle sensoron the basis of the candidate positioning information, so as to obtainthe plurality of candidate map semantic features under the coordinatesystem; then the observation semantic features are matched with theremaining candidate map semantic features, so as to obtain matchingpairs.

In some embodiments, in order to improve the correct rate of mapmatching or increase the number of effective matching features, it isspecified in this embodiment that the matching pairs satisfy thefollowing condition 1 to condition 4:

-   -   Condition 1: the candidate map semantic feature is in one-to-one        correspondence with the observation semantic feature matched        therewith. That is, one candidate map semantic feature can be        only matched with one observation semantic feature, and one        observation semantic feature can be only matched with one        candidate map semantic feature.    -   Condition 2: a semantic label of the candidate map semantic        feature is the same as a semantic label of the observation        semantic feature matched therewith.    -   Condition 3: a semantic-Euclidean distance between the candidate        map semantic feature and the observation semantic feature        matched therewith is less than or equal to a Euclidean distance        threshold. The semantic-Euclidean distance is used for        representing a similarity between one candidate map semantic        feature and one observation semantic feature; and the Euclidean        distance threshold is inversely correlated with a Euclidean        distance between the candidate map semantic feature and the        candidate positioning information. The Euclidean distance        between the candidate map semantic feature and the candidate        positioning information may be understood as the Euclidean        distance between the position (coordinate value) corresponding        to the candidate map semantic feature and the candidate        positioning information (coordinate value).

In some embodiments, the Euclidean distance threshold is determined bythe following formula:

th=th₀×f(t)

wherein, th is the Euclidean distance threshold, th₀ is a set fixedprior threshold, t is the Euclidean distance between the candidate mapsemantic feature and the candidate positioning information, and f(t) isa mapping function inversely correlated with t. The greater theEuclidean distance t between the candidate map semantic feature and thecandidate positioning information (i.e., the farther the candidate mapsemantic feature from the candidate positioning information), the morelikely it is to cause matching errors. Therefore, the smaller theEuclidean distance threshold th is, the higher the probability ofcorrect matching.

-   -   Condition 4: the semantic-Euclidean distance between the        candidate map semantic features and the observation semantic        feature matched therewith is minimum among all        semantic-Euclidean distances corresponding to the candidate map        semantic features, and is minimum among all semantic-Euclidean        distances corresponding to the observation semantic features. In        some embodiments, one semantic-Euclidean distance is calculated        for the candidate map semantic features and each observation        semantic feature. In some embodiments, a certain        semantic-Euclidean distance is minimum in the plurality of        semantic-Euclidean distances corresponding to the candidate        semantic feature, but is not the minimum in the plurality of        semantic-Euclidean distances corresponding to the observation        semantic features, and then the two are not matching pairs.

In some embodiments, the matching pairs also need to satisfy thefollowing condition 5:

-   -   Condition 5: when the candidate map semantic feature has a        superior semantic feature, a semantic label of the superior        semantic feature of the candidate map semantic feature is the        same as a semantic label of a superior semantic feature of the        observation semantic feature matched therewith. In some        embodiments, a semantic-Euclidean distance between the superior        semantic feature of the candidate map semantic feature and the        superior semantic feature of the observation semantic feature        matched therewith is less than or equal to the Euclidean        distance threshold.

In some embodiments, the superior semantic feature characterizes overallinformation of the target for positioning, and a subordinate semanticfeature characterizes local or endpoint of the target for positioning.For example, the superior semantic feature is a lane line, and thesubordinate semantic feature is an endpoint of the lane line.

The purpose of setting the condition 5 is to reduce the probability ofmatching errors, for example, to reduce the probability that an endpointsimilar to a certain lane line matches an endpoint of another lane line.

It should be noted that some matching algorithms, such as nearestneighbor matching or violent matching, cannot satisfy the aboveconditions 1 to 5.

In some embodiments, the matching unit 403 determines asemantic-Euclidean distance between any candidate map semantic featureand any observation semantic feature through the following modes:

-   -   when a semantic label of the observation semantic feature is        different from a semantic label of the candidate map semantic        feature, the semantic-Euclidean distance is an invalid value        INF;    -   when the semantic label of the observation semantic feature is        the same as the semantic label of the candidate map semantic        feature:    -   a Euclidean distance between a coordinate value corresponding to        the observation semantic feature and a coordinate value        corresponding to the candidate map semantic feature and a        Euclidean distance threshold are determined;    -   when the Euclidean distance between the coordinate value        corresponding to the observation semantic feature and the        coordinate value corresponding to the candidate map semantic        feature is less than or equal to the Euclidean distance        threshold, the semantic-Euclidean distance is the Euclidean        distance between the coordinate value corresponding to the        observation semantic feature and the coordinate value        corresponding to the candidate map semantic feature; and    -   when the Euclidean distance between the coordinate value        corresponding to the observation semantic feature and the        coordinate value corresponding to the candidate map semantic        feature is greater than the Euclidean distance threshold, the        semantic-Euclidean distance is the invalid value INF.

In some embodiments, the Euclidean distance threshold is determined bythe following formula:

$\left\{ \begin{matrix}{d = {INF}} & {{label} \neq {label}^{\prime}} \\\left\{ \begin{matrix}{d = {de}} & {d<={th}} \\{d = {INF}} & {d > {th}}\end{matrix} \right. & {{label} = {label}^{\prime}}\end{matrix} \right.$

-   -   wherein d is the semantic-Euclidean distance; INF is an infinite        value (i.e., invalid value); th is the Euclidean distance        threshold; de=f(m, m′), f(m, m′)is an Euclidean distance        calculation function, and a specific form thereof is related to        a geometric morphology of m and m′, for example, the distance        measurement from a point to a point, and the distance        measurement from a line to a line are different, but all can        obtain an Euler distance value. m is the observation semantic        feature, m′ is the candidate map semantic feature, label is the        semantic label of the observation semantic feature, and label'        is the semantic label of the candidate map semantic feature.

In some embodiments, in order to satisfy the condition 5, when thesemantic label of the observation semantic feature is the same as thesemantic label of the candidate map semantic feature, and theobservation semantic feature or the candidate map semantic feature hasthe superior semantic feature, it is judged whether the semantic labelof the superior semantic feature of the observation semantic feature isthe same as the semantic label of the candidate map semantic feature,and when the semantic label of the superior semantic feature of theobservation semantic feature is not the same as the semantic label ofthe candidate map semantic feature, it is determined that thesemantic-Euclidean distance between the observation semantic feature andthe map semantic feature is the invalid value INF.

In some embodiments, the matching unit 403 may determine asemantic-Euclidean distance matrix composed of the plurality ofobservation semantic features and the plurality of candidate mapsemantic features. In the semantic-Euclidean distance matrix shown inFIG. 7 , m1, m2, m3, m4 and m5 are observation semantic features, whilem′1, m′2, m′3, m′4 and m′5 are candidate map semantic features. In FIG.7 , a semantic-Euclidean distance between m1 and m′1 is 0.5, and asemantic-Euclidean distance between m1 and m′2 is INF; and asemantic-Euclidean distance between m1 and m′3 is 0.1. It can be seenthat each element in the semantic-Euclidean distance matrix shown inFIG. 7 represents a semantic-Euclidean distance. In some embodiments,there is an invalid value INF in the semantic-Euclidean distance matrixshown in FIG. 7 , and therefore, the semantic-Euclidean distance matrixis a sparse matrix, which does not need to store the invalid value INF,but only needs to store an effective value, thereby improving subsequentmatching efficiency.

In some embodiments, the matching unit 403 may determine a distanceranking matrix on the basis of the semantic-Euclidean distance matrix,each element in the distance ranking matrix being a 2-tuple, and the2-tuple representing ranking of rows and columns on which thesemantic-Euclidean distance is located, wherein the smaller the distanceis, the smaller the ranking value is, and the ranking value of 1represents the closest distance. The distance ranking matrix as shown inFIG. 8 is a distance ranking matrix determined on the basis of thesemantic-Euclidean distance matrix shown in FIG. 7 . For example, inFIG. 7 , since the ranking of rows and columns on which thesemantic-Euclidean distance is located between m1 and m′1 are 1 and 3respectively, the value of elements corresponding to m1 and m′1 in FIG.are (1,3).

In some embodiments, the matching unit 403 may determine the observationsemantic features and the candidate map semantic features correspondingto a 2-tuple of (1, 1) in the distance ranking matrix as the matchingpair as a matching pair. For example, in FIG. 8 , m1 and m′3 aredetermined as the matching pair, while m5 and m′5 are also determined asthe matching pair.

In some embodiments, after the determining the observation semanticfeature and the candidate map semantic feature corresponding to the2-tuple of (1, 1) in the distance ranking matrix as the matching pair,the matching unit 403 modifies all elements of rows and columnscorresponding to the 2-tuple of (1, 1) into the invalid value INF, andupdates the distance ranking matrix.

FIG. 9 is an example diagram of a matrix obtained after updating thedistance ranking matrix shown in FIG. 8 . In order to satisfy thecondition 1, all elements of rows and columns corresponding to the2-tuple of (1, 1) in FIG. 8 are modified to the invalid value INF, andthe elements modified as INF make values of the elements in the same rowand column thereof change to form a new matrix as shown in FIG. 9 . Itshould be noted that the modification to the invalid value INF is merelyan intermediate operation of updating the distance ranking matrix, anddoes not represent that the semantic-Euclidean distance is the invalidvalue INF.

In some embodiments, the matching unit 403 may determine the observationsemantic feature and the candidate map semantic feature corresponding tothe 2-tuple of (1, 1) in the updated distance ranking matrix as thematching pair. For example, in FIG. 9 , m3 and m′2, and m4 and m′4 arealso determined as the matching pairs.

The matching unit 403 may repeatedly update the distance ranking matrixand determine the matching pair until no 2-tuple of (1, 1) is present inthe updated distance ranking matrix.

In some embodiments, in any matching scenario, as long as themeasurement distance is defined, the distance matrix may be constructed,and then matching is performed to obtain the matching result. Therefore,the matching manner based on the distance matrix provided in theembodiments of the present application has wide applicability.

Second Determining Unit 404

The second determining unit 404 is configured for determining theoptimal candidate positioning information of the vehicle and thematching pair corresponding to the optimal candidate positioninginformation on the basis of the matching pairs corresponding to eachpiece of candidate positioning information. In some embodiments, theoptimal candidate positioning information may be used as the initialpositioning information, so as to perform map matching on theobservation semantic features obtained in real time.

In some embodiments, the second determining unit 404 selects candidatepositioning information with the maximum number of matching pairs as theoptimal candidate positioning information of the vehicle.

In some embodiments, the second determining unit 404 may determine anevaluation value of each piece of candidate positioning information onthe basis of prior contribution degrees of different candidate mapsemantic features to vehicle positioning and the matching pairscorresponding to each piece of candidate positioning information. Theprior contribution degree may be pre-configured, for example, thecontribution of ground semantic feature such as lane lines, and thelike, to vehicle positioning is greater than that of non-groundfeatures, such as a guideboard, and the like. Therefore, the priorcontribution degree of the lane line may be set to be higher than theprior contribution degree of the road sign.

In some embodiments, the evaluation value of the candidate positioninginformation is determined by the following formula:

score=λ_(c) Σc _(i)+λ_(d) Σf(d _(i))

wherein, score is the evaluation value of the candidate positioninginformation; λ_(c) and λ_(d) are prior distribution weights, thespecific values of which are not defined in this embodiment; c_(i) is aprior contribution degree of the candidate map semantic feature in ani^(th) matching pair to vehicle positioning; d_(i) is asemantic-Euclidean distance of the i^(th) matching pair; and f(d_(i)) isa mapping function inversely correlated with d_(i), that is, the smallerthe d_(i), the greater the f(d_(i)).

In some embodiments, the second determining unit 404 may selectcandidate positioning information with the maximum evaluation value asthe optimal candidate positioning information of the vehicle.

In some embodiments, after selecting the candidate positioninginformation with the maximum evaluation value, the second determiningunit 404 judges whether the maximum evaluation value is less than anevaluation value threshold, and when the maximum evaluation value isless than the evaluation value threshold, it is determined that the mapmatching is failed. The evaluation value threshold is a priori value,that is, a predetermined value, the specific value of which is notdefined in this embodiment, and a person skilled in the art can set theevaluation value threshold according to actual needs.

In some embodiments, the division of each unit in the map matchingapparatus 400 is only a logical function division, and there may beother division modes in actual implementation. For example, theacquisition unit 401, the first determining unit 402, the matching unit403 and the second determining unit 404 may be realized as one unit; theacquisition unit 401, the first determining unit 402, the matching unit403 or the second determining unit 404 may also be divided into aplurality of subunits. It may be understood that each system orsubsystem can be realized by electronic hardware or a combination ofcomputer software and electronic hardware. Whether the functions areexecuted by hardware or software depends on particular applications anddesign constraint conditions of the technical solutions. Those skilledin the art can use different methods for each specific application torealize the described functions.

FIG. 5 is a structural schematic diagram of an electronic deviceprovided by the embodiments of the present application.

As shown in FIG. 5 , the electronic device includes: at least oneprocessor 501, at least one memory 502 and at least one communicationinterface 503. Various components in the electronic device are coupledtogether through a bus system 504. The communication interface 503 isused for information transfer with external devices. It can beunderstood that the bus system 504 is configured to realizecommunications between these components. The bus system 504 includes apower bus, a control bus, and a status signal bus in addition to a databus. However, for the sake of clarity, various buses are designated asthe bus system 504 in FIG. 5 .

It can be understood that the memory 502 in this embodiment may be avolatile memory or a non-volatile memory, or may include both volatileand non-volatile memories.

In some embodiments, the memory 502 is stored with the followingelements, executable modules or data structures, or subsets thereof, ortheir extensions: an operating system and an application program.

The operating system includes various system programs, such as aframework layer, a core library layer, a driver layer, etc., which areused to realize various basic services and handle hardware-based tasks.The application programs including various application programs, such asMedia Player, Browser, etc. are used to implement various applicationtasks. The program for implementing the map matching method of theembodiments of the present application may be included in theapplication program.

In the embodiment of the present application, the processor 501 is usedto execute the steps of each embodiment of the map matching methodprovided by the embodiments of the present application by calling aprogram or instruction stored in the memory 502, specifically, a programor instruction stored in the application program.

The map matching method provided in the embodiments of the presentapplication may be applied to a processor 501 or implemented by theprocessor 501. The processor 501 may be an integrated circuit chip witha signal processing capacity. In an implementation process, the steps inthe foregoing methods may be completed using an integrated logic circuitof hardware in the processor 501 or an instruction in a form ofsoftware. The processor 501 may be a general-purpose processor, aDigital Signal Processor (DSP), an Application Specific IntegratedCircuit (ASIC), a Field Programmable Gate Array (FPGA) or otherprogrammable logic device, discrete gate or transistor logic device, anddiscrete hardware component. The general-purpose processor may be amicroprocessor or the processor may be any conventional processor, orthe like.

Steps of the map matching method provided by the embodiments of thepresent application may be directly executed and accomplished by meansof a hardware decoding processor or may be executed and accomplishedusing a combination of hardware and software modules in the decodingprocessor. The software unit may be located in a mature storage mediumin the art, such as a random access memory, a flash memory, a read-onlymemory, a programmable read-only memory, or an electrically erasableprogrammable memory, a register, and the like. The storage medium islocated in the memory 502. The processor 501 reads information from thememory 502 and completes the steps of the foregoing method incombination with the hardware of the processor.

FIG. 6 is an exemplary flowchart of a map matching method provided bythe embodiments of the present application. An executing body of themethod is an electronic device, and in some embodiments, the executingbody of the method may also be an intelligent driving system supportedby an on-vehicle device. For ease of description, in the followingembodiments, the electronic device is used as an executing body todescribe the flow of the map matching method.

As shown in FIG. 6 , in step 601, the electronic device acquires initialpositioning information of a vehicle and vehicle sensor data.

In step 602, the electronic device determines a plurality of pieces ofcandidate positioning information on the basis of the initialpositioning information.

In step 603, the electronic device determines a plurality of observationsemantic features on the basis of the vehicle sensor data.

In step 604, the electronic device acquires local map information on thebasis of the initial positioning information, the local map informationincluding a plurality of map semantic features.

In step 605, the electronic device, for each piece of candidatepositioning information:

-   -   6051: converts the plurality of map semantic features into a        coordinate system of the vehicle sensor on the basis of the        candidate positioning information, so as to obtain a plurality        of candidate map semantic features under the coordinate system;        and    -   6052: matches the plurality of observation semantic features        with the plurality of candidate map semantic features, so as to        obtain matching pairs.

In step 606, the electronic device determines optimal candidatepositioning information of the vehicle and a matching pair correspondingto the optimal candidate positioning information on the basis of thematching pairs corresponding to each piece of candidate positioninginformation.

In some embodiments, before matching the plurality of observationsemantic features with the plurality of candidate map semantic features,the method further includes:

-   -   removing candidate map semantic features in a blind area of the        vehicle sensor.

In some embodiments, the matching pairs satisfy the following condition1 to condition 4:

-   -   Condition 1: the candidate map semantic feature is in one-to-one        correspondence with the observation semantic feature matched        therewith. That is, one candidate map semantic feature can be        only matched with one observation semantic feature, and one        observation semantic feature can be only matched with one        candidate map semantic feature.    -   Condition 2: a semantic label of the candidate map semantic        feature is the same as a semantic label of the observation        semantic feature matched therewith.    -   Condition 3: a semantic-Euclidean distance between the candidate        map semantic feature and the observation semantic feature        matched therewith is less than or equal to a Euclidean distance        threshold. The semantic-Euclidean distance is used for        representing a similarity between one candidate map semantic        feature and one observation semantic feature; and the Euclidean        distance threshold is inversely correlated with a Euclidean        distance between the candidate map semantic feature and the        candidate positioning information.    -   Condition 4: the semantic-Euclidean distance between the        candidate map semantic features and the observation semantic        feature matched therewith is minimum among all        semantic-Euclidean distances corresponding to the candidate map        semantic features, and is minimum among all semantic-Euclidean        distances corresponding to the observation semantic features.

In some embodiments, the matching pairs also satisfy the followingcondition 5:

-   -   Condition 5: when the candidate map semantic feature has a        superior semantic feature, a semantic label of the superior        semantic feature of the candidate map semantic feature is the        same as a semantic label of a superior semantic feature of the        observation semantic feature matched therewith.

In some embodiments, a semantic-Euclidean distance between the superiorsemantic feature of the candidate map semantic feature and the superiorsemantic feature of the observation semantic feature matched therewithis less than or equal to the Euclidean distance threshold.

In some embodiments, a semantic-Euclidean distance between any candidatemap semantic feature and any observation semantic feature is determinedby the following modes:

-   -   when a semantic label of the observation semantic feature is        different from a semantic label of the candidate map semantic        feature, the semantic-Euclidean distance is an invalid value        INF;    -   when the semantic label of the observation semantic feature is        the same as the semantic label of the candidate map semantic        feature:    -   a Euclidean distance between a coordinate value corresponding to        the observation semantic feature and a coordinate value        corresponding to the candidate map semantic feature and a        Euclidean distance threshold are determined;    -   when the Euclidean distance between the coordinate value        corresponding to the observation semantic feature and the        coordinate value corresponding to the candidate map semantic        feature is less than or equal to the Euclidean distance        threshold, the semantic-Euclidean distance is the Euclidean        distance between the coordinate value corresponding to the        observation semantic feature and the coordinate value        corresponding to the candidate map semantic feature; and    -   when the Euclidean distance between the coordinate value        corresponding to the observation semantic feature and the        coordinate value corresponding to the candidate map semantic        feature is greater than the Euclidean distance threshold, the        semantic-Euclidean distance is the invalid value INF.

In some embodiments, the Euclidean distance threshold is determined bythe following formula:

th=th₀ ×f(t)

wherein, th is the Euclidean distance threshold, th₀ is a set fixedprior threshold, t is the Euclidean distance between the candidate mapsemantic feature and the candidate positioning information, and f(t) isa mapping function inversely correlated with t.

In some embodiments, when the semantic label of the observation semanticfeature is the same as the semantic label of the candidate map semanticfeature, and the observation semantic feature or the candidate mapsemantic feature has the superior semantic feature, it is judged whetherthe semantic label of the superior semantic feature of the observationsemantic feature is the same as the semantic label of the candidate mapsemantic feature, and when the semantic label of the superior semanticfeature of the observation semantic feature is not the same as thesemantic label of the candidate map semantic feature, it is determinedthat the semantic-Euclidean distance between the observation semanticfeature and the map semantic feature is the invalid value INF.

In some embodiments, the matching the plurality of observation semanticfeatures with the plurality of candidate map semantic features, so as toobtain the matching pairs, includes:

-   -   determining a semantic-Euclidean distance matrix composed of the        plurality of observation semantic features and the plurality of        candidate map semantic features;    -   determining a distance ranking matrix on the basis of the        semantic-Euclidean distance matrix; each element in the distance        ranking matrix being a 2-tuple, and the 2-tuple representing        ranking of rows and columns on which the semantic-Euclidean        distance is located; and    -   determining the observation semantic feature and the candidate        map semantic feature corresponding to a 2-tuple of (1, 1) in the        distance ranking matrix as the matching pair.

In some embodiments, after the determining the observation semanticfeature and the candidate map semantic feature corresponding to the2-tuple of (1, 1) in the distance ranking matrix as the matching pair,the method further includes:

-   -   modifying all elements of rows and columns corresponding to the        2-tuple of (1, 1) into the invalid value INF, and updating the        distance ranking matrix;    -   determining the observation semantic feature and the candidate        map semantic feature corresponding to the 2-tuple of (1, 1) in        the updated distance ranking matrix as the matching pair; and    -   repeatedly executing the above two steps until no 2-tuple of        (1, 1) is present in the updated distance ranking matrix.

In some embodiments, the determining the optimal candidate positioninginformation of the vehicle and the matching pair corresponding to theoptimal candidate positioning information on the basis of the matchingpairs corresponding to each piece of candidate positioning information,includes:

-   -   selecting candidate positioning information with the maximum        number of matching pairs as the optimal candidate positioning        information of the vehicle.

In some embodiments, the determining the optimal candidate positioninginformation of the vehicle and the matching pair corresponding to theoptimal candidate positioning information on the basis of the matchingpairs corresponding to each piece of candidate positioning information,includes:

-   -   determining an evaluation value of each piece of candidate        positioning information on the basis of prior contribution        degrees of different candidate map semantic features to vehicle        positioning and the matching pairs corresponding to each piece        of candidate positioning information; and    -   selecting candidate positioning information with the maximum        evaluation value as the optimal candidate positioning        information of the vehicle.

In some embodiments, the evaluation value of the candidate positioninginformation is determined by the following formula:

score=λ_(c) Σc _(i)+λ_(d) Σf(d _(i))

wherein, score is the evaluation value of the candidate positioninginformation, λ_(c) and λ_(d) are prior distribution weights, c_(i) is aprior contribution degree of the candidate map semantic feature in ani^(th) matching pair to vehicle positioning, d_(i) is asemantic-Euclidean distance of the i^(th) matching pair, and f(d_(i)) isa mapping function inversely correlated with d_(i).

In some embodiments, after selecting the candidate positioninginformation with the maximum evaluation value, the method furtherincludes:

-   -   judging whether the maximum evaluation value is less than an        evaluation value threshold, and when the maximum evaluation        value is less than the evaluation value threshold, it is        determined that the map matching is failed.

It should be noted that, for the sake of simple description, all theforegoing method embodiments are all expressed as a series of actioncombinations, but those skilled in the art should understand that theembodiments of the present application are not limited by the describedaction sequences, because certain steps may be performed in othersequences or concurrently according to the embodiments of the presentapplication. Moreover, those skilled in the art can understand that theembodiments described in the specification are all optional embodiments.

The embodiments of the present application also provide a non-transientcomputer-readable storage medium, which stores programs or instructions,and the programs or instructions cause a computer to execute the stepsof the various embodiments of the map matching method, which will not berepeated here to avoid repeated descriptions.

It should be noted that the terms “including”, “comprising” or anyvariations thereof are intended to embrace a non-exclusive inclusion,such that a process, a method, an article, or an apparatus including aseries of elements, includes not only those elements but also includesother elements not expressly listed, or also incudes elements inherentto such process, method, article, or apparatus. In the absence offurther limitation, an element defined by the phrase “including . . . ”does not exclude the existence of additional identical elements in theprocess, method, article, or apparatus that includes the element.

Those skilled in the art can understand that although some embodimentsdescribed herein include some features included in other embodimentsrather than other features, but combinations of features of differentembodiments are meant to be within the scope of the present applicationand form different embodiments.

Those skilled in the art can understand that the description of eachembodiment has its own emphasis. For parts not detailed in oneembodiment, please refer to the related description of otherembodiments.

Although the embodiments of the present application have been describedwith reference to the drawings, those skilled in the art can makevarious modifications and variations without departing from the spiritand scope of the present application, and such modifications andvariations all fall within the scope defined by the appended claims.

Industrial Applicability

The embodiments of the present application are suitable for a vectorsemantic map, achieves real-time matching of observation features andmap features by only using vector information (such as information ofmap semantic features) of the vector semantic map, does not depend onadditional data such as additional descriptors and intensity, andachieves a good matching effect on the basis of reducing storagerequirements and computing power use. In addition, the embodiments ofthe present application have no special requirements for sensor types (acamera, a laser radar, or the like, may be applicable). The presentapplication has industrial applicability.

What is claimed is:
 1. A map matching method, comprising: acquiringinitial positioning information of a vehicle and vehicle sensor data;determining a plurality of pieces of candidate positioning informationbased on the initial positioning information; determining a plurality ofobservation semantic features based on the vehicle sensor data;acquiring local map information based on the initial positioninginformation, wherein the local map information comprises a plurality ofmap semantic features; for each piece of candidate positioninginformation: converting the plurality of map semantic features into acoordinate system of the vehicle sensor based on the candidatepositioning information to obtain a plurality of candidate map semanticfeatures under the coordinate system; and matching the plurality ofobservation semantic features with the plurality of candidate mapsemantic features to obtain matching pairs, wherein the matching pairssatisfy that: when the candidate map semantic feature has a superiorsemantic feature, a semantic-Euclidean distance between the superiorsemantic feature of the candidate map semantic feature and a superiorsemantic feature of the observation semantic feature matched with thecandidate map semantic feature is less than or equal to a Euclideandistance threshold; and determining optimal candidate positioninginformation of the vehicle and a matching pair corresponding to theoptimal candidate positioning information based on the matching pairscorresponding to each piece of candidate positioning information.
 2. Themap matching method according to claim 1, wherein, before the step ofmatching the plurality of observation semantic features with theplurality of candidate map semantic features, the map matching methodfurther comprises: removing candidate map semantic features in a blindarea of the vehicle sensor.
 3. The map matching method according toclaim 1, wherein the matching pairs satisfy the following conditions:the candidate map semantic feature is in one-to-one correspondence withthe observation semantic feature matched with the candidate map semanticfeature; a semantic label of the candidate map semantic feature is thesame as a semantic label of the observation semantic feature matchedwith the candidate map semantic feature; a semantic-Euclidean distancebetween the candidate map semantic feature and the observation semanticfeature matched with the candidate map semantic feature is less than orequal to the Euclidean distance threshold; the semantic-Euclideandistance is configured for representing a similarity between onecandidate map semantic feature and one observation semantic feature; andthe Euclidean distance threshold is inversely correlated with aEuclidean distance between the candidate map semantic feature and thecandidate positioning information; and the semantic-Euclidean distancebetween the candidate map semantic feature and the observation semanticfeature matched with the candidate map semantic feature is the smallestamong semantic-Euclidean distances corresponding to the candidate mapsemantic feature, and is the smallest among semantic-Euclidean distancescorresponding to the observation semantic feature.
 4. The map matchingmethod according to claim 3, wherein the matching pairs further satisfythe following conditions: when the candidate map semantic feature hasthe superior semantic feature, a semantic label of the superior semanticfeature of the candidate map semantic feature is the same as a semanticlabel of the superior semantic feature of the observation semanticfeature matched with the candidate map semantic feature.
 5. (canceled)6. The map matching method according to claim 3, wherein asemantic-Euclidean distance between any candidate map semantic featureand any observation semantic feature is determined by the followingmodes: when the semantic label of the observation semantic feature isdifferent from the semantic label of the candidate map semantic feature,the semantic-Euclidean distance is an invalid value INF; when thesemantic label of the observation semantic feature is the same as thesemantic label of the candidate map semantic feature: a Euclideandistance between a coordinate value corresponding to the observationsemantic feature and a coordinate value corresponding to the candidatemap semantic feature and the Euclidean distance threshold aredetermined; when the Euclidean distance between the coordinate valuecorresponding to the observation semantic feature and the coordinatevalue corresponding to the candidate map semantic feature is less thanor equal to the Euclidean distance threshold, the semantic-Euclideandistance is the Euclidean distance between the coordinate valuecorresponding to the observation semantic feature and the coordinatevalue corresponding to the candidate map semantic feature; and when theEuclidean distance between the coordinate value corresponding to theobservation semantic feature and the coordinate value corresponding tothe candidate map semantic feature is greater than the Euclideandistance threshold, the semantic-Euclidean distance is the invalid valueINF.
 7. The map matching method according to claim 3, wherein theEuclidean distance threshold is determined by the following formula:th=th₀ ×f(t) wherein, th is the Euclidean distance threshold, th₀ is aset fixed prior threshold, t is the Euclidean distance between thecandidate map semantic feature and the candidate positioninginformation, and f(t) is a mapping function inversely correlated with t.8. The map matching method according to claim 6, wherein when thesemantic label of the observation semantic feature is the same as thesemantic label of the candidate map semantic feature, and theobservation semantic feature or the candidate map semantic feature hasthe superior semantic feature, it is judged whether a semantic label ofthe superior semantic feature of the observation semantic feature is thesame as the semantic label of the candidate map semantic feature, andwhen the semantic label of the superior semantic feature of theobservation semantic feature is not the same as the semantic label ofthe candidate map semantic feature, it is determined that thesemantic-Euclidean distance between the observation semantic feature andthe candidate map semantic feature is the invalid value INF.
 9. The mapmatching method according to claim 3, wherein the step of matching theplurality of observation semantic features with the plurality ofcandidate map semantic features to obtain the matching pairs comprises:determining a semantic-Euclidean distance matrix composed of theplurality of observation semantic features and the plurality ofcandidate map semantic features; determining a distance ranking matrixbased on the semantic-Euclidean distance matrix, wherein each element inthe distance ranking matrix is a 2-tuple, and the 2-tuple represents aranking of rows and columns, wherein the semantic-Euclidean distance islocated on the rows and columns; and determining the observationsemantic feature and the candidate map semantic feature corresponding toa 2-tuple of (1, 1) in the distance ranking matrix as the matching pair.10. The map matching method according to claim 9, wherein after the stepof determining the observation semantic feature and the candidate mapsemantic feature corresponding to the 2-tuple of (1, 1) in the distanceranking matrix as the matching pair, the map matching method furthercomprises: 1) modifying elements of the rows and columns correspondingto the 2-tuple of (1, 1) into the invalid value INF, and updating thedistance ranking matrix to obtain an updated distance ranking matrix; 2)determining the observation semantic feature and the candidate mapsemantic feature corresponding to the 2-tuple of (1, 1) in the updateddistance ranking matrix as the matching pair; and 3) repeatedlyexecuting steps 1) and 2) until no 2-tuple of (1, 1) is present in theupdated distance ranking matrix.
 11. The map matching method accordingto claim 1, wherein the step of determining the optimal candidatepositioning information of the vehicle and the matching paircorresponding to the optimal candidate positioning information based onthe matching pairs corresponding to each piece of candidate positioninginformation comprises: selecting candidate positioning information witha maximum number of the matching pairs as the optimal candidatepositioning information of the vehicle.
 12. The map matching methodaccording to claim 1, wherein the step of determining the optimalcandidate positioning information of the vehicle and the matching paircorresponding to the optimal candidate positioning information based onthe matching pairs corresponding to each piece of candidate positioninginformation comprises: determining an evaluation value of each piece ofcandidate positioning information based on prior contribution degrees ofdifferent candidate map semantic features to vehicle positioning and thematching pairs corresponding to each piece of candidate positioninginformation; and selecting candidate positioning information with amaximum evaluation value as the optimal candidate positioninginformation of the vehicle.
 13. The map matching method according toclaim 12, wherein the evaluation value of each piece of candidatepositioning information is determined by the following formula:score=λ_(c) Σc _(i)+λ_(d) Σf(d _(i)) wherein, score is the evaluationvalue of each piece of candidate positioning information, λ_(c) andλ_(d) are prior distribution weights, c_(i) is a prior contributiondegree of the candidate map semantic feature in an i^(th) matching pairto vehicle positioning, d_(i) is a semantic-Euclidean distance of thei^(th) matching pair, and f(d_(i)) is a mapping function inverselycorrelated with d_(i).
 14. The map matching method according to claim12, wherein after the step of selecting the candidate positioninginformation with the maximum evaluation value, the map matching methodfurther comprises: judging whether the maximum evaluation value is lessthan an evaluation value threshold, and when the maximum evaluationvalue is less than the evaluation value threshold, it is determined thata map matching is failed.
 15. (canceled)
 16. An electronic device,comprising: a processor and a memory, wherein the processor isconfigured for executing the steps of the map matching method accordingto claim 1 by calling a program or instruction stored in the memory. 17.A non-transient computer-readable storage medium, wherein thenon-transient computer-readable storage medium stores a program orinstruction, and the program or instruction enables a computer toexecute the steps of the map matching method according to claim 1.